Flipping the Enterprise AI Stack with SQL: The Surprising Key to Useful AI Agents

Agentic AI promises to complete business tasks across systems such as CRMs, ERPs, databases, and cloud applications, but its effectiveness depends on secure, reliable access to distributed enterprise data. The proposed architecture uses SQL as a common language because large language models are extensively familiar with its standardized syntax, making SQL generation generally more practical than handling numerous proprietary APIs. CData connectors translate SQL queries and commands into the API calls required by more than 300 business systems, while the Model Context Protocol (MCP) provides a secure channel through which AI agents can access those connectors. This approach supports both retrieving live data and performing actions such as creating, updating, or deleting records, while applying the user’s existing permissions, enabling validation before changes, and producing auditable records of activity. By placing the AI agent as the primary interface and using CData and MCP to manage system-specific integrations, the model aims to simplify development, preserve security controls, and enable agents to act across an organization’s software environment.

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